Maritime search and rescue operations planning: A hybrid approach for multi-asset deconfliction and efficient resource allocation

Abstract

Decision support systems for maritime search and rescue search planning, such as those available in Canada and in the US, assist search mission coordinators in generating search plans for missing objects. However, because these systems rely on simulations to evaluate a plan’s probability of success, they cannot be directly integrated into traditional mathematical programming frameworks. Furthermore, existing tools, such as SAR Optimizer, the planning module within the Canadian Advanced Search Planning Tool (CANSARP), lack the mechanisms to enforce some operational constraints and do not optimize the number of search resources and search durations. To address these limitations, we propose a hybrid optimization framework that treats the search simulator as an external blackbox objective function calculator. Within this framework, we develop models that enforce horizontal spatial separation between search units, an important operational safety constraint. Moreover, we introduce search duration as a decision variable, allowing for search plans that consume fewer search units or reduce search times. Numerical experiments on realistic scenarios demonstrate that this hybrid integration of simulation and optimization not only enhances safety but also significantly improves efficiency, potentially saving hundreds of nautical miles in flight time.